
behavioral nutritional auditing
v1by FitnessGrid
A non-prescriptive coaching framework focused on self-monitoring, pattern recognition, and behavioral self-regulation for nutritional change.
Get an AI coach that uses this skill
FitnessGrid is an AI coach that plans your week and adapts as you go. Install behavioral nutritional auditing and your coach will follow this protocol every week, learn from what you actually do, and adjust on the fly.
- Your coach builds the week from this skill
- Adapts to your actual progress, not a static template
- Free to start — no credit card, ~60 seconds to set up
Procedure
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Establish Awareness (Phase 1: Baseline)
- Use
get_user_historyandrender_meal_logto review the last 3-7 days of intake. - Focus on non-judgmental logging. If the user is overwhelmed, suggest lower-friction methods like photo logs or 24-hour recalls.
- Identify 3–5 repeatable themes (e.g., protein/plant gaps, late-night energy surges, or hydration levels) using
get_insights.
- Use
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Conduct Pattern Audit
- Audit logs for "Balance and Timing" and "Micronutrient Patterns" rather than binary "good/bad" choices.
- Inquire about the "ABCDEF" frame: Anthropometric (weight/circumference), Biochemical, Clinical, Dietary, Exercise, and Fotos. Treat these as neutral data points for context.
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Incorporate Behavioral Inquiry
- Apply Motivational Interviewing (MI) by asking open-ended questions to elicit "change talk" (e.g., "How would it feel if you had more energy in the afternoon?").
- Use Cognitive Behavioral Therapy (CBT) principles to identify emotional triggers or "all-or-nothing" cognitive distortions recorded in meal notes or history.
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Define Small Wins (Phase 2: Autonomy)
- Collaborate with the user to select one "small, realistic change" based on their identified patterns.
- Use
create_noteto document these behavioral experiments (e.g., adding a specific snack to reduce fatigue).
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Transition to Mastery (Phase 3: Scaling)
- Move focus from numerical calorie targets to visual portion guides (Hand and Plate method).
- Coach the user to use "palm of protein" or "fist of vegetables" to reduce numerical obsession and simplify self-regulation.
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Monitor Energy and Mood
- Audit the results of behavioral experiments by checking user feedback on perceived energy, mood, and non-scale wins.
- Use
update_noteto track long-term habit stability and refine the approach as the user moves toward maintenance (Phase 4).
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Neutralize Feedback
- Frame all metrics (weight, macros) as "guide rails" to inform the process, not as strict compliance hurdles.
- Direct any "all-or-nothing" thinking back to the "how" and "why" of eating behaviors.